Digital Commons Network^™

Combination Therapy With 308-Nm Excimer Laser, Compound Glycyrrhizin, And Tacrolimus For Pediatric Facial And Cervical Vitiligo, Yingying Qian, Yafen Wu, Wei Li, Qian Chen, Siyuan Song, Ting Zhang

Faculty, Staff and Students Publications

Background: Vitiligo is a common acquired depigmenting disorder in pediatric dermatology. Facial and cervical involvement is particularly distressing due to cosmetic disfigurement, leading to psychosocial impairment in children and significant psychological burden for parents. Safe and effective treatment strategies to halt disease progression and promote repigmentation are urgently needed.

Objective: To evaluate the clinical efficacy, onset of repigmentation, safety, and repigmentation patterns of 308-nm excimer laser combined with oral compound glycyrrhizin and topical 0.03% tacrolimus in children with facial and cervical vitiligo.

Methods: A total of 112 pediatric patients were randomized into two groups: treatment group (n=56, 86 lesions) received …

3d Geometric Modelling And Clinical Validation Of Lower Limb And Preliminary 2-Year Post-Operative Clinical Outcomes Of Neutral Boundary Alignment In Total Knee Arthroplasty, Bruno Violante, Lorenzo Deveza, Francesco Pollara, Giovanni Rusconi, Gianroberto Ferreri, Alessandro Annibaldi, Ilwhan Park

Faculty, Staff and Students Publications

Purpose: To introduce a novel three-dimensional (3D) modelling concept for lower limb alignment in total knee arthroplasty (TKA), termed neutral boundary alignment (NBA) and to present preliminary clinical data supporting this physics-based custom alignment strategy.

Methods: NBA is defined as the alignment at which the knee joint line is parallel to the ground during the mid-stance phase of gait, achieving static equilibrium with the NBA axis aligned to gravity. A theoretical geometric model of the lower limb was developed, integrating principles of physics to describe this condition. Preliminary clinical validation was performed using radiographic data from an initial cohort of …

Functional Analysis Of Genes Enriched In Male Reproductive Organs: Validation Via Fertility Assessment Of 14 Knockout Mouse Lines, Kazumasa Takemoto, Katarzyna Kent, Kaori Nozawa, Shingo Tonai, Ahn H Pham, Terumi Fujimoto, Thomas X Garcia, Martin M Matzuk, Masahito Ikawa, Keizo Tokuhiro

Faculty, Staff and Students Publications

Tissue-restricted gene expression serves as a key indicator of functional importance within specific organs. Despite advances in high-throughput sequencing and genome editing over the past decade, this principle remains the gold standard for identifying candidate genes for functional studies. In reproductive biology, testis-restricted expression is anticipated to indicate genes crucial for gametogenesis, given the conserved features shared between spermatogenesis and oogenesis. Concurrently, rapid evolution of spermatogenesis-related genes is well documented and thought to arise through duplication and subsequent mutation of ubiquitously expressed genes. In this study, we established 14 knockout (KO) mouse lines for male reproductive organ-enriched genes, all of …

Highly Variable Expressivity Of A Cnv Deletion Involving Tbx4 In Three Deceased Siblings With Lung Developmental Disorder And Their Mildly Affected Mother And Grandfather, Przemyslaw Szafranski, Tomasz Gambin, Michal Kadlof, Michał Denkiewicz, Dariusz Plewczynski, Hyun Jeong Kim, Gail Deutsch, Nahir Cortes-Santiago, Salmo Raskin, Paweł Stankiewicz

Faculty, Staff and Students Publications

Single nucleotide variants (SNVs) and copy-number variant (CNV) deletions involving TBX4 have been associated with pulmonary arterial hypertension, ischiocoxopodopatellar syndrome, and lethal lung developmental disorders (LLDDs). Thus far, all large CNV deletions encompassing entire TBX4 have been found to have arisen de novo. Here, we present a three-generation family with three neonate siblings who died within 35-66 days due to histopathologically diagnosed LLDD. Whole-genome sequencing identified an ~108-kb CNV deletion encompassing TBX4 in all three infants. The deletion was also found in their mother with a history of pneumonia and persistent thick upper airway secretions and in the maternal grandfather …

Noradrenergic Innervation Across Brain Regions Is Altered By Aging And By Disease Progression In A Mouse Model Of Alzheimer’S Disease Neuropathology, Nicole M Hernandez, Manuel Silva-Pérez, Jeannie Chin

Faculty, Staff and Students Publications

Norepinephrine plays critical roles in modulating arousal and attention, is highly dynamic in awake, behaving individuals, and has anti-inflammatory and neuroprotective actions. Notably, the locus coeruleus (LC), the primary source of norepinephrine in the central nervous system, is among the first brain regions to show pathological alterations in early stages of Alzheimer's disease (AD). LC neuronal loss and associated reductions in norepinephrine in the brain have therefore been postulated to play a key role in AD pathophysiology. LC neurons and their axons have been studied in several mouse models of AD-related neuropathology to investigate their contribution to brain dysfunction in …

Frontline Aspiration Versus Stent Retriever Thrombectomy For M2 Occlusions: Insights From The Star Registry, Michael Gaub, Rahim Abo Kasem, Ilko Maier, Ansaar Rai, Pascal Jabbour, Joon-Tae Kim, Brian Howard, Ali Alawieh, Stacey Quintero Wolfe, Robert M Starke, Marios-Nikos Psychogios, Amir Shaban, Nitin Goyal, Justin Dye, Ali Alaraj, Mohamad Ezzeldin, Shinichi Yoshimura, David Fiorella, Omar Tanweer, Daniele G Romano, Pedro Navia, Hugo Cuellar, Isabel Fragata, Adam Polifka, Joshua Osbun, Fazeel Siddiqui, Mark Moss, Kaustubh Limaye, Maxim Mokin, Charles Matouk, Min S Park, Waleed Brinjikji, Ergun Daglioglu, Richard Williamson, David J Altschul, Christopher S Ogilvy, Roberto Crosa, Michael R Levitt, Benjamin Gory, Alexandra Paul, Peter Kan, Walter Casagrande, Shakeel Chowdhry, Michael F Stiefel, Ramesh Grandhi, Alejandro Spiotta, Justin Mascitelli

Faculty, Staff and Students Publications

Background: Recent trials have furthered uncertainty regarding the endovascular benefit for medium vessel occlusions (MeVO). Stent retrievers (SR) were employed in the first attempt in most interventional arm participants. We sought to compare outcomes in acute MCA M2 occlusions between frontline aspiration and SR, and to delineate procedural and anatomical covariates associated with differential treatment effect.

Methods: Retrospective analysis of a multicenter stroke thrombectomy cohort identified cases of MT for M2 occlusions. Unmatched and propensity score-matched (PSM) cohorts were generated comparing frontline aspiration to standalone and combined SR. The primary outcome was functional independence (mRS 0-2) at 90 days. Recanalization, …

Comparing Rotational Thromboelastometry And Standard Coagulation Assays For Predicting Intraoperative Bleeding In Pediatric Liver Transplantation, Kirby Deshotels, Trung Nguyen, Jun Teruya, Muhammed Umair M Mian, Kelby Fuller, Sanjiv Harpavat, Anna Banc-Husu, Amir Navaei, John Goss, Moreshwar Desai, Arun Saini

Faculty, Staff and Students Publications

Background: Utility of preoperative rotational thromboelastometry (ROTEM) over standard coagulation assays (SCAs) in predicting intraoperative bleeding during orthotopic liver transplantation (OLT) in children with liver failure (LF) remains unclear.

Methods: In this single-center retrospective cohort of pediatric OLT recipients, we compared the predictive values of preoperative ROTEM parameters (intrinsic pathway (INTEM) maximum clot firmness (MCF), extrinsic pathway (EXTEM) clotting time (CT), INTEM CT, and fibrinogen-based (FIBTEM) MCF) and the corresponding SCAs (platelet count, international normalized ratio (INR), activated partial thromboplastin time (aPTT), and fibrinogen level, respectively) for significant intraoperative bleeding (blood loss of > 22 mL/kg; i.e., ≥ 85th percentile for …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Integration Of Hybrid Quantum-Neuromorphic Ai With Cloud, Edge, And High-Performance Computing Environments, Arun B. Prasad, Ajay Prasad, Dineshkumar Rajendran, Anurag Tiwari, T. Akilan, Islombek Khushvaktov

Computer Science Faculty Publications

The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows …

Cognitive Prosthetic: An Ai-Enabled Multimodal System For Episodic Recall In Knowledge Work, Lawrence Obiuwevwi, Krzystof J. Rechowicz, Vikas Ashok, Sachin Shetty, Sampath Jayarathna

Computer Science Faculty Publications

Modern knowledge workplaces increasingly strain human episodic memory as individuals navigate fragmented attention, overlapping meetings, and multimodal information streams. Existing workplace tools provide partial support through note-taking or analytics but rarely integrate cognitive, physiological, and attentional context into retrievable memory representations. This paper presents the Cognitive Prosthetic Multimodal System (CPMS)—an AI-enabled proof-of-concept designed to support episodic recall in knowledge work through structured episodic capture and natural language retrieval. CPMS synchronizes speech transcripts, physiological signals, and gaze behavior into temporally aligned, JSON-based episodic records processed locally for privacy. Beyond data logging, the system includes a web-based retrieval interface that allows users …

Exploring Marshall–Olkin Models Through Bibliometric And Topic Modeling Approaches Uses Latent Dirichlet Allocation (1981-2025): A Study Based On Scopus Data, Humberto Llinás, Brian Llinás, Carlos López, Daniela Nuñez

Computer Science Faculty Publications

The Marshall–Olkin family of distributions has gained increasing attention in fields such as reliability engineering, survival analysis, financial risk modeling, and actuarial science because of its flexibility in modeling dependence among events and its wide range of extensions. Despite its growing relevance, a systematic understanding of how research on Marshall–Olkin models has evolved over time is still limited. This study addresses this gap by combining bibliometric techniques with topic modeling to analyze the structure and evolution of the scientific literature on Marshall–Olkin models. The analysis includes all 266 peer-reviewed publications on Marshall–Olkin models indexed in Scopus between 1981 and 2025. …

Stochastic Fractional-Order Memristive Fuzzy Bam Neural Networks With Time Delays And Leakage Term For Finite-Time Stability Analysis, J. Kumar, M. Syed Ali, Sumaya Sanober, Mohammad Yarish, Abeer M. Alotaibi, Tarek F. Ibrahim

Computer Science Faculty Publications

In this study, a finite-time stability analysis with time delays and a leakage term is conducted on stochastic fractional-order memristive fuzzy BAM neural networks. FOMFBAMNNs are developed using set-valued map theories as well as differential inclusion. We obtained several significant adequate criteria of uniform stability in the mean square of such networks by using analytical methods and inequality approaches, such as Cauchy–Schwarz inequality and Burkholder–Davis–Gundy inequality. In addition to examining two different fractional-order derivatives between the U-layer and V-layer synchronously with fractional order, the existence, uniqueness, and stability of its equilibrium point are also shown ½ ≤ α ≤ 1. …

Towards Supporting Real-Time Estimation Of Vehicle Fuel Consumption And Co2 Emissions In Smart City Applications, Abrar Alali, Stephan Olariu

Computer Science Faculty Publications

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously …

Contextual Scaffolding And Self-Efficacy: Supporting Computer Skill Development Among Blind Learners In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Inclusive computer literacy education efforts, broadening the participation of blind or visually impaired (BVI) individuals, have gained traction in recent years. Existing literature investigating these efforts primarily draws evidence from affluent Global North contexts, where accessibility resources and legal frameworks are relatively more mature. Little is known about the in-situ teaching and learning challenges faced by trainers and BVI students, respectively, in resource-constrained, multicultural Global South countries like India. To address this knowledge gap, we conducted a four-month contextual inquiry at two computer training centers catering to 94 BVI students in India. We notably observed a rigid, experience-driven training environment …

Open Scholarly Information Systems: Status Quo, Challenges, Opportunities, Hannah Bast, Guillaume Cabanac, Paolo Manghi, Jian Wu, Marcel R. Ackermann

Computer Science Faculty Publications

Over the past 30 years, a rich ecosystem of scholarly information systems has developed that openly provide their services to the scientific community. These systems include aggregators of bibliographic metadata (e.g., DBLP, OpenCitations, OpenAIRE Graph, OpenAlex, ORKG, Semantic Scholar, CiteSeerX, and CORE); publication, data, and software repositories (e.g., Arxiv.org, Figshare, Zenodo, Software Heritage, and Dataverse); and PID authorities (e.g., ORCID, ROR, Crossref, and DataCite). This interdisciplinary Dagstuhl Seminar "Open Scholarly Information Systems: Status Quo, Challenges, Opportunities" (25381) was the first of its kind to bring together practitioners from this ecosystem, as well as researchers investigating related questions or relying on …

Finding The Signal In The Noise: An Exploratory Study On Assessing The Effectiveness Of Ai And Accessibility Forums For Blind Users' Support Needs, Satwik Ram Kodandaram, Jiawei Zhou, Xiaojun Bi, Iv Ramakrishnan, Vikas Ashok

Computer Science Faculty Publications

Accessibility forums and, more recently, generative AI tools have become vital resources for blind users seeking solutions to computer-interaction issues and learning about new assistive technologies, screen reader features, tutorials, and software updates. Understanding user experiences with these resources is essential for identifying and addressing persistent support gaps. Towards this, we interviewed 14 blind users who regularly engage with forums and GenAI tools. Findings revealed that forums often overwhelm users with multiple overlapping topics, redundant or irrelevant content, and fragmented responses that must be mentally pieced together, increasing cognitive load. GenAI tools, while offering more direct assistance, introduce new barriers …

Explainable Convolutional Neural Network Model Provides An Alternative Genome-Wide Association Perspective On Mutations In Sars-Cov-2, Parisa C. Hatami, Richard Annan, Luis Miranda, Jane L. Gorman, Mengjun Xie, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Identifying informative genomic features in SARS-CoV-2 can help clarify patterns of viral evolution. In this study, we developed an explainable convolutional neural network (CNN) model to classify SARS-CoV-2 genomic sequences into the WHO-designated Variants of Concern (VOCs), Alpha, Beta, Gamma, Delta, and Omicron. Using a balanced dataset of genomes, the classification CNN achieved 99.96% accuracy on the held-out test set. To interpret the model’s predictions, we applied SHapley Additive exPlanations (SHAP) to estimate the contribution of each nucleotide position to VOC-label prediction and compared aggregated attributions with a chi-square GWAS baseline applied to the same categorical labels. SHAP prioritized several …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Guidelines For Automatic Grading Of Student Essays Using Large Language Models, Diwakar Yalpi, Sruta Keerti Kasula, Ravi Mukkamala

Computer Science Faculty Publications

Automated essay evaluation using large language models (LLMs) has emerged as a promising approach to support scalable and consistent educational assessment. However, the effectiveness of LLM-based grading varies significantly across evaluation dimensions and is highly influenced by prompt design and model selection. In this study, we evaluate five state-of-the-art LLMs across five rubric-based categories: Relevance to Question, Reasoning and Critical Thinking, Evidence and Examples, Organization, and Clarity and Writing Quality. We systematically investigate the impact of three prompting strategies, including rubric-only prompting, exemplar-based prompting (with and without rubric guidance)(Original and Refined prompt designs) incorporating structured instructions. Additionally, a prompt ablation …

Toward An Event-Level Analysis Of Hadron Structure Using Differential Programming, Kevin Braga, Markus Diefenthaler, Steven Goldenberg, Daniel Lersch, Yaohang Li, Jian-Wei Qiu, Kishansingh Rajput, Felix Ringer, Nobuo Sato, Malachi Schram

Computer Science Faculty Publications

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental quantum correlation functions (QCFs) that characterize the microscopic structure of hadronic systems within the theory of QCD. Recent advances in scientific computing and machine learning have opened new avenues for addressing this challenge using …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

A Survey On Generative Ai For Detector Effects Unfolding In Particle And Nuclear Physics, Tareq Alghamdi, Tommaso Vittorini, Jitao Xu, Marco Battaglieri, Derek I. Glazier, Glòria Montaña, Giorgio Foti, Alessandro Pilloni, Nobuo Sato, Yaohang Li

Computer Science Faculty Publications

In particle and nuclear physics, “detector effects unfolding” can be viewed as a highdimensional inverse problem whose goal is to recover the true event distributions from observed experimental data corrupted by detector-induced distortions. Recent advances in generative AI have positioned data-driven and machine learning-based approaches as powerful alternatives to traditional unfolding techniques, offering superior scalability to high-dimensional data, capability of learning complex detector responses, and the ability to operate directly at the event level. We survey state-of the-art generative AI-based models for detector folding and unfolding. We review existing architectures and training strategies, and highlight recent methodological advances and open …

Replicatorbench: Benchmarking Llm Agents For Replicability In Social And Behavioral Sciences, Bang Nguyen, Dominik Soós, Qian Ma, Rochana R. Obadage, Zack Ranjan, Sai Koneru, Timothy M. Errington, Shakhlo Nematova, Sarah Rajtmajer, Jian Wu, Meng Jiang

Computer Science Faculty Publications

The literature has witnessed an emerging interest in developing and evaluating AI agents for automated assessment of research claims in scientific papers. Existing benchmarks focus primarily on the computational aspect of this task, testing agents' ability to reproduce or replicate research outcomes when having access to the code and data. This setting, while foundational, (1) fails to capture the inconsistent availability of new data for replication as opposed to reproduction, and (2) lacks ground-truth diversity by focusing exclusively on fully reproducible or replicable papers, thereby failing to evaluate an agent's ability to identify non-replicable research. Furthermore, most benchmarks only evaluate …

Toward A Centralized Cross Domain Database For Reproducibility And Replicability Studies, Rochana R. Obadage, Sarah Rajtmajer, Jian Wu

Computer Science Faculty Publications

Reproducibility and replicability (R&R) are structural properties of scientific knowledge, yet existing R&R evidence remains fragmented across domains and initiatives. We present an ongoing effort to develop a centralized, cross-domain database of R&R studies that links published works to their corresponding R&R attempts and supporting assessments. Bibliographic metadata and title-based matching through scholarly indexing services identify canonical records and persistent identifiers. A heuristic, uncertainty-aware matching algorithm supports intra-source and inter-source deduplication, complemented by manual review of ambiguous cases. A unified schema accommodates heterogeneous assessment frameworks and records original studies, R&R studies, assessments, aggregated summaries, and source provenance. The database currently …

Organ Chips And Translational Research: Identifying And Examining New Ethical Issues, Melanie Jeske

Center for Medical Ethics and Health Policy Staff Publications

Organ chips, also known as organ-on-a-chip devices, tissue chips, or microphysiological systems, have emerged over the last decade as a promising translational technology amidst growing concern about the translational crisis between laboratory research and patient bedside. Pointing to high rates of failure between nonhuman animal models and safety and efficacy in humans, organ chips and similar new approach methods have attracted substantial public and private investment. As human-cell-based alternatives to animal models, organ chips promise more predictive, efficient, and ethical platforms for pharmaceutical and toxicity testing. Engineered cultivation systems that enable cells to assemble into tissue-like structures (e.g. kidney, brain, …

Anything But Endo: Diagnostic Buck-Passing In Endometriosis Diagnosis, Rita Dexter, Megan Kitts, Heather Welty, Melanie Jeske

Center for Medical Ethics and Health Policy Staff Publications

People living with endometriosis, a disease in which tissue similar to the lining of the uterus grows elsewhere in the body, often experience prolonged diagnostic journeys because of symptom variability, normalisation of period pain and other symptoms, and lack of awareness of the condition. In this article, we analyse the endometriosis diagnostic journey through the lens of epistemic injustice. Drawing on in-depth interviews with 52 people living with endometriosis in the United States, we introduce the concept of diagnostic buck-passing to characterise the phenomenon wherein individuals who seek treatment for their symptoms end up stuck in a cycle of seeing …

Putting The L In Elsi: Legal Methods For Bioethics Research, Anya E R Prince, Benjamin Berkman, Donald Ford, Dov Fox, Christi Guerrini, Amy Koopmann, Natalie Ram, Jessica L Roberts, Kayte Spector-Bagdady, Sonia Suter

Center for Medical Ethics and Health Policy Staff Publications

Lawyers and law professors are increasingly involved in interdisciplinary scientific teams and grant research to answer ethical, legal and policy questions related to biomedical topics. Yet, the methods that lawyers use to conduct legal research and analysis are not always familiar to scientists and social scientists conducting peer review of a proposed project with legal aims or a publication reporting a legal study. To better facilitate interdisciplinary ethical, legal, and social implications collaboration, there is a need to better explain how legal research methodologies can provide robust tools to address a range of nuanced biomedical questions. This paper explores …

Differences Between Government, Consortium, And Private Database Stewards Impacting The Genomic Data Market: A Survey Of U.S. Academic Genetic Researchers., Amanda K Greene, J Denard Thomas, Kaitlyn Jaffe, Luyun Chen, Kerry A Ryan, Brian J Zikmund-Fisher, J Scott Roberts, Amy L Mcguire, Katherine Hendy, Kayte Spector-Bagdady

Center for Medical Ethics and Health Policy Staff Publications

Background: Despite major shifts in U.S. federal government data sharing requirements, their impact, and relation to researcher choice of database, are underexplored. This study surveyed genetic researchers regarding trends, priorities, perceptions of quality, impact on research outcomes, and genomic data sharing and use across government, consortium, and private databases.

Methods: As part of an exploratory sequential mixed methods project, we surveyed 294 U.S.-based genomic academic researchers.

Results: Genetic researchers generally have a choice between databases, which allows them to prioritize data quality. This might explain recent trends toward the use of government and consortium databases away from private ones. Respondents …

Can I Get A Witness? The Ethical Dimensions Of Family Presence In Patient Suffering, Jennifer Blumenthal-Barby, Trevor M Bibler, Holland Kaplan, Adam Omelianchuk, Joanna Smolenski

Center for Medical Ethics and Health Policy Staff Publications

For patients who are suffering, the bedside presence of a family member can provide comfort, and many people hold that there is moral value in being present with a conscious, suffering patient. Yet what is the moral significance of the absence of family members when a patient is minimally conscious or unconscious and not aware of their absence? Clinicians are often troubled when family members and surrogate decision-makers who are able to spend a significant amount of time at an unconscious, seriously ill patient's bedside do not do so. Clinicians feel frustrated that they must bear the burden of witnessing …